quickstart¶
Date: Dec 01, 2021 Version:
Getting Started¶
A minimal experiment definition supplies the path to the dataset, the name of the column that holds the time index and the name of the column that holds the target to predict. In the minimal example below, an experiment is conducting using the retail sales data included with divina and using the log link function best suited towards sales data.
{
"experiment_definition": {
"target": "Sales",
"link_function": "log",
"time_index": "Date",
"data_path": "divina://retail_sales"
}
}
As you can see, Divina automatically uses the non-target data in the file to make insample predictions that are quite accurate. However, the forecast produced is for all stores in aggregate while there are three distinct retail locations in the dataset.
Target Dimensions
Below we use the target_dimensions option to tell divina to individually aggregate and forecast each retail store in the dataset.
{
"experiment_definition": {
"target": "Sales",
"link_function": "log",
"target_dimensions": [
"Store"
],
"time_index": "Date",
"data_path": "divina://retail_sales"
}
}
We can see through the interpretability interface what information is influencing the forecasts and how.